In practical factorial experiments, we sometimes find that complete randomization of the order runs is infeasible because it more difficult to change levels some factors than others, especially in engineering experiments. Then, fractional split-plot (FFSP) designs represent a option such situations. The difficult-to-change are called whole plots (WP) factors, and other subplot (SP) factors. WP ...

Fractional factorial split-plot design has been widely used in many fields due to its advantage of saving experimental cost. The general minimum lower order confounding criterion is usually as one the attractive for selecting fractional design. In this paper, we are interested theoretical construction methods optimal designs under criterion. We present several conditions.

Microarray applications for the study of gene expression are becoming accessible for researchers in more and more systems. Applications from field or laboratory experiments are often complicated by the need to superimpose sample pairing for two-color arrays on experimental designs that may already be complex. For example, split-plot designs are commonly used in biological systems where experime...

Abstract This research aims to study the effect of number harvest rows under two digging depths and cultivars on quality harvesting potatoes (damage losses). The experiment was factorial, using a randomised complete block design with split-split plot three replications for each treatment. types were placed in main plot, type digger secondary split depth plot. results showed no significant diffe...

To compare several promising product designs manufacturers must measure their performance under multiple environmental conditions In many applications a product design is considered to be seriously awed if its performance is poor for any level of the environmental factor For example if a particular automobile battery design does not function well under temperature extremes then a manufacturer m...

To compare several promising product designs, manufacturers must measure their performance under multiple environmental conditions. In many applications, a product design is considered to be seriously flawed if its performance is poor under any level of the environmental factor. For example, if a particular automobile battery design does not function well under some temperature conditions, then...

Binomial data are often generated in split-plot experimental designs in agricultural, biological, and environmental research. Modeling non-normality and random effects are the two major challenges in analyzing binomial data in split-plot designs. In this study, seven statistical methods for testing whole-plot and subplot treatment effects using mixed, generalized linear, or generalized linear m...

Complete randomization in many industrial and agricultural experiments is frequently impractical due to constraints in time, cost or existence of one or more hard-to-change factors. In these situations, restrictions on randomization lead to split-plot designs (SPD), allowing certain factor levels to be randomly applied to the whole plot units and remaining factor levels randomly to the subplot ...

The worm plot is a series of detrended Q-Q plots, split by covariate levels. The worm plot is a diagnostic tool for visualizing how well a statistical model fits the data, for finding locations at which the fit can be improved, and for comparing the fit of different models. This paper shows how the worm plot can be used in conjunction with quantile regression. No parametric distributional assum...